I'm having some troubles to "suggest" ARIMA models based only in the series plot, autocorrelation and partial autocorrelation function.
When the series have trend I know that is not stationary and need differencing, but in this case there is no trend. Apparently this series is nonstationary because the mean look bigger at start, but how can I simply by looking at the series identify that it has mean and constant variance?
This is some slides that I'm using Forecasting using R. There is some statement that say "The ACF of stationary data drops to zero relatively quickly", but how fast? In this case the ACF drops fast, but in some point the autocorrelatios become negative significant.
The plot of Partial Autocorrelation Function shows significant values at lag 1, 2 and 12.
I should consider a model with 1 difference?
What I think in general:
The mean at first seems to me to be higher than in the other parts of the process, which indicates that the series is not stationary.
The behavior of ACF and PACF looks like an Autoregressive Model